Habitat: A Platform for Embodied AI Research I will present Habitat, a platform for research in embodied artificial intelligence (AI). Habitat enables training embodied agents (virtual robots) in highly efficient photorealistic 3D simulation, before transferring the learned skills to reality. The ‘software stack’ for training embodied agents involves datasets providing 3D assets, simulators that render these assets and simulate agents, and tasks that define goals and evaluation metrics, enabling us to benchmark scientific progress. We aim to standardize this entire stack by contributing specific instantiations at each level: unified support for scanned and designed 3D scene datasets, a new simulation engine (Habitat-Sim), and a modular API (Habitat-API). The Habitat architecture and implementation combine modularity and high performance. For example, when rendering a realistic scanned scene from the Matterport3D dataset, Habitat-Sim achieves several thousand frames per second (FPS) running single-threaded and can reach over 10,000 FPS multi-process on a single GPU! These large-scale engineering contributions enable us to answer scientific questions requiring experiments that were till now impracticable or `merely' impractical. Finally, I will describe the Habitat Challenge, an autonomous navigation challenge that aims to benchmark and advance efforts in embodied AI.